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Agglomerative Learning for General Fuzzy Min-Max Neural Network

By Bogdan Gabrys

Abstract

In this paper an agglomerative learning algorithm based on similarity measures defined for hyperbox fuzzy sets is proposed. It is presented in a context of clustering and classification problems that are tackled using a general fuzzy min-max (GFMM) neural network. The agglomerative scheme's robust behaviour in the presence of noise and outliers and its insensitivity to the order of the training pattern presentation are used as a complementary features to an incremental learning scheme, making it more suitable for online adaptation and dealing with large training data set

Topics: aintel, csi
Publisher: IEEE
Year: 2000
OAI identifier: oai:eprints.bournemouth.ac.uk:9646

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